Reinforcement Learning & RLHF Explained: Why ChatGPT Feels Helpful

Última actualización septiembre de 2026

6Lecciones

BeginnerNivel

50 minDuración

GratisCertificado

Find out why ChatGPT, Claude, and Gemini feel helpful, polite, and safe instead of just completing text. A short, no-code course on reinforcement learning, supervised fine-tuning, reward models, PPO and DPO, and the side effects of alignment like sycophancy and reward hacking. The natural next step after How LLMs Actually Work.

Contenido del Curso

3 módulos • 6 lecciones

1

Learning From Rewards

2 lecciones

Reinforcement Learning: Learning by Trial, Error, and Reward

Why a Base Model Is Not a Helpful Assistant

2

How RLHF Works

3 lecciones

Step 1: Supervised Fine-Tuning, Learning From Demonstrations

Step 2: The Reward Model, Teaching a Model to Judge

Step 3: Optimizing With PPO, and the Simpler DPO Shortcut

3

What Alignment Means for You

1 lecciones

The Side Effects: Sycophancy, Reward Hacking, and Over-Refusal

Reseñas

Inicia sesión para dejar una reseña

Reasoning Models Explained: When AI "Thinks" (No Code)
Beginner
Some AI models pause to "think" before they answer. Learn what reasoning models actually do, why thinking step by step helps, how models are trained to reason, when a reasoning model is worth the extra time and cost, how to prompt one well, and where their thinking still goes wrong. No code, no math.
Transformers and Attention Explained: The Engine Inside Every LLM
Intermediate
A short, no-code, no-math explainer of the attention mechanism that powers ChatGPT, Claude, and Gemini. Understand query, key, and value, why models use many attention heads, how word order is encoded, and why transformers read your whole prompt at once.
How LLMs Actually Work: Tokens, Parameters, and MoE Explained
Beginner
Go one level under the hood of ChatGPT, Claude, and Gemini. Understand tokens, next-token prediction, context windows, model parameters, Mixture of Experts, and why AI models cost money to run. A short, no-code course for people who use AI every day.
Advanced Prompt Engineering: Evaluation, Meta-Prompting & Structured Outputs
Intermediate
Move beyond writing prompts to engineering them. Build evals and rubrics to measure prompt quality objectively, use AI to write and refine your prompts, get reliable structured outputs, and run optimization loops that cut variance, cost, and latency.
AI Hardware Explained: GPUs, Memory, and Data Centers (No Code)
Beginner
Learn what physically happens when AI runs. See which parts of a computer matter for AI, why GPUs and AI chips took over, why memory is the real limit on model size and speed, how quantization shrinks models, and what goes on inside the data centers that train and serve them. No code, no math.
How Diffusion Models & Image Generation Actually Work
Beginner
Understand how tools like Midjourney, DALL-E, and Stable Diffusion actually generate an image, in plain language with no code and no math. Learn what a diffusion model is, how starting from noise leads to a finished picture, what latent space is, how your prompt steers the process, and why diffusion beat older methods like GANs.